Hearing loss is one of the most prevalent chronic conditions, affecting 10% of the U.S. population. Although signal amplification by modern hearing aids makes sound more audible to hearing impaired listeners, speech understanding in background noise remains one of the biggest challenges in hearing prosthesis. The proposed research seeks a solution to this challenge by developing a speech segregation system that can significantly improve intelligibility of noisy speech for listeners with hearing loss, with the loner term goal of applying to hearing aid design. Unlike traditional speech enhancement and beam forming algorithms, the proposed monaural (one-microphone) solution will be grounded in perceptual principles of auditory scene analysis. There are two stages in auditory scene analysis: A simultaneous organization stage that groups concurrent sound components and a sequential organization stage that groups sound components across time. This project is designed to achieve three specific aims. The first aim is to improve word recognition scores of hearing-impaired listeners in background noise. The second and the third aims are to improve the sentence-level intelligibility scores in background noise and in interfering speech, respectively. To achieve the first aim, a simultaneous organization algorithm will be developed that uses the pitch cue to segregate voiced speech and the onset and offset cues to segregate unvoiced speech. To achieve aims 2 and 3, a sequential organization algorithm will be developed that groups simultaneously organized streams across time to produce a sentence segregated from background interference. Sequential organization will be performed by analyzing pitch characteristics and a novel clustering method on the basis of incremental speaker modeling. A set of seven speech intelligibility experiments involving both hearing-impaired and normal-hearing listeners will be conducted to systematically evaluate the developed system.